Research On Internet Public Opinion Monitoring And Warning For Online Travel Service

As an emerging product of the Internet,online travel services have a profound impact on people's lives,bringing convenience to people's lives,but also negative impacts on social development,such as lack of credibility in the travel service platform,bundled sales,imparity clause,false publicity,poor after-sales service as well as process loopholes.Moreover,it has become a new normal for netizens to express their views and opinions through new media.Therefore,how to strengthen the supervision of public opinion and to correctly guide public opinion has become a difficult problem for online travel service suppliers.This study will establish a public opinion management system through network public opinion monitoring and early warning,and employ information and text extraction,text processing,BP neural network and other technologies to stream the prevention mechanism,and further boost the standardized management of public opinion information of online travel service enterprises.First of all,this research summarizes related literatures ranging from online public opinion,online travel,BP neural network and other technologies,and defines the core concepts such as network public opinion monitoring and network public opinion warning.Secondly,according to the business needs of online travel service enterprises to deal with network public opinion,the system is divided into five modules: information collection,information application,information retrieval,data center and business display.The business display falls into nine functions: public opinion summary,public opinion center,public opinion analysis,public opinion monitoring,public opinion warning,public opinion report,public opinion work,auxiliary tools,and system setting.This is designed to prevent the loss of control,reduce social risks,and provide scientific decision-making support for tourism.enterprises.Thirdly,according to the requirements of the "public opinion early warning" function constructed by the system,the network public opinion early warning system is constructed from three dimensions and eight aspects respectively,and the work flow of public opinion early warning,BP neural network learning process,normalization processing and output state are analyzed.Finally,combined with the BP neural network in MatLab tool,the empirical research on the event of "The falsified data of MaFengWo" is conducted.By employing network public opinion mornitoring of online travel services,the paper displays the development trends,article sources,carrier distribution and emotional attributes of public opinion events in the form of charts.In addition,the data were normalized based on early-warning indicators,and the Train function of the MatLab neural network Train was used to Train the data.The Sim function was used to check the accuracy of the data,and the output state was taken as an important basis to deal with the network public opinion.This paper is centered on online tourism public opinion monitoring and warning,On the one hand,for tourism service enterprises,first of all,it can better meet the needs of daily public opinion monitoring and early warning,and effectively grasp the public opinion dynamics of the industry.Secondly,it can track and analyze the information of related industries in real time,and play the role of competitive intelligence system to a certain extent.On the other hand,for tourism authorities,it can be used to understand the current situation of tourism industry,and to foster strengths and circumvent weaknesses with the help of the elastic adjustment of development strategy.Secondly,according to the appeals of the majority of travel enthusiasts to travel service industry,effective supervision will be adopted to travel service companies for the avoidance of tourism crisis.